roberts_mag — 2D `edges` op

• Data kinds: image → image

• Call: fullseye.apply(img, "roberts_mag", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: roberts (the HALCON reference is a useful guide to its meaning and parameters)

Usage

Gradient magnitude via the Roberts cross gradient (edge detection). Equivalent to HALCON's `roberts` (Detect edges using the Roberts filter.).

> The detailed description below is the original text — the summary and the headings are translated.

2×2 の対角差分（`v - shift(-1,-1) と shift(0,-1) - shift(-1,0)）のユークリッドノルムを _norm で正規化する。a, b は未使用。カーネルが小さい（2×2）ためノイズに敏感だが応答の局在性は高い。境界は _shift_edge` によりレプリケート（折り返しなし）。

Detailed usage guide

• gallery2d_edges family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• gallery2d_edges — py -3.11 examples/gallery2d_edges.py

Ops the type connects to (they accept `image` as input)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`edges`)

sobel_mag · prewitt_mag · dog · grad_dir · log · corner_response · sk_scharr · sk_farid

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

roberts_mag — 2D edges op